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*
!.gitignore
!*.ipynb
!*.py

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*
!.gitignore
!*.ipynb
!*.py
!*.jpg
!synset.txt
!README.md

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*
!*.ipynb
!.gitignore
!*.py
*_pb2.py
!requirements.txt

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python
*.pb
*.pbtxt

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# Package Information for pkg-config

prefix=/Applications/Julia-0.6.app/Contents/Resources/julia
exec_prefix=${prefix}
libdir=${exec_prefix}/lib
includedir=${prefix}/include/julia

Name: Julia
Description: A fresh approach to technical computing 
Version: 0.6

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/*
 * Adapt from this example: http://docs.opencv.org/3.0-beta/modules/core/doc/command_line_parser.html
 */
#include "opencv2/core.hpp"
#include "opencv2/core/cvstd.hpp" // cv::String
#include <iostream>

using namespace cv;
using std::cout;
using std::cerr;

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#!/usr/bin/env python3
# -*- coding: utf8 -*-
#https://stackoverflow.com/questions/29731726/how-to-calculate-a-gaussian-kernel-matrix-efficiently-in-numpy
import numpy as np
from scipy.ndimage import convolve

def gauss_kernel(size=5, sigma=None):
    if sigma is None:
        sigma = size/5
    xs = np.arange(-size//2 + 1, size//2 + 1)

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#!/usr/bin/env python3
# -*- coding: utf8 -*-
#https://stackoverflow.com/questions/29731726/how-to-calculate-a-gaussian-kernel-matrix-efficiently-in-numpy
import numpy as np

def gauss_kernel(size=5, sigma=None):
    if sigma is None:
        sigma = size/5
    xs = np.arange(-size//2 + 1, size//2 + 1)
    grid_x, grid_y = np.meshgrid(xs, xs)

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#!/usr/bin/env python3
# -*- coding: utf8 -*-
import sys, argparse
try:
    import cv2
    import numpy as np
    from scipy.ndimage import convolve
except ImportError:
    print("install opencv and scipy please", file=sys.stderr)
    sys.exit(1)

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#!/usr/bin/env python3
from __future__ import print_function
import sys
try:
    import cv2
    import numpy as np
except ImportError:
    print("install cv2 and numpy please.", file=sys.stderr)
    sys.exit(1)